znsoftm/YOLO-Nano

A new version YOLO-Nano

YOLO-Nano

A new version YOLO-Nano inspired by NanoDet.

In this project, you can enjoy:

• a different version of YOLO-Nano

Network

This is a a different of YOLO-Nano built by PyTorch:

• Backbone: ShuffleNet-v2
• Neck: a very lightweight FPN+PAN

Train

• Batchsize: 32
• Base lr: 1e-3
• Max epoch: 120
• LRstep: 60, 90
• optimizer: SGD

The overview of my YOLO-Nano

Experiment

Environment:

• Python3.6, opencv-python, PyTorch1.1.0, CUDA10.0,cudnn7.5
• For training: Intel i9-9940k, RTX-2080ti

VOC:

YOLO-Nano-1.0x:

	size	mAP
VOC07 test	320	65.0
VOC07 test	416	69.1
VOC07 test	608	70.8

COCO:

	size	AP	AP50	AP75	AP_S	AP_M	AP_L
COCO eval	320	17.2	33.1	16.2	2.6	16.0	31.7
COCO eval	416	19.6	36.9	18.6	4.6	19.1	33.3
COCO eval	608	20.6	38.6	19.5	7.0	22.5	30.7

YOLO-Nano-0.5x:

hold on ...

Visualization

On COCO-val

The overview of my YOLO-Nano

Installation

• Pytorch-gpu 1.1.0/1.2.0/1.3.0
• Tensorboard 1.14.
• opencv-python, python3.6/3.7

Dataset

VOC Dataset

I copy the download files from the following excellent project: https://github.com/amdegroot/ssd.pytorch

I have uploaded the VOC2007 and VOC2012 to BaiDuYunDisk, so for researchers in China, you can download them from BaiDuYunDisk:

Link：https://pan.baidu.com/s/1tYPGCYGyC0wjpC97H-zzMQ

Password：4la9

You will get a VOCdevkit.zip, then what you need to do is just to unzip it and put it into data/. After that, the whole path to VOC dataset is data/VOCdevkit/VOC2007 and data/VOCdevkit/VOC2012.

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

MSCOCO Dataset

I copy the download files from the following excellent project: https://github.com/DeNA/PyTorch_YOLOv3

Download MSCOCO 2017 dataset

Just run sh data/scripts/COCO2017.sh. You will get COCO train2017, val2017, test2017.

Train

VOC

python train.py -d voc --cuda -v [select a model] -ms

You can run python train.py -h to check all optional argument.

COCO

python train.py -d coco --cuda -v [select a model] -ms

Test

VOC

python test.py -d voc --cuda -v [select a model] --trained_model [ Please input the path to model dir. ]

COCO

python test.py -d coco-val --cuda -v [select a model] --trained_model [ Please input the path to model dir. ]

Evaluation

VOC

python eval.py -d voc --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

COCO

To run on COCO_val:

python eval.py -d coco-val --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

To run on COCO_test-dev(You must be sure that you have downloaded test2017):

python eval.py -d coco-test --cuda -v [select a model] --train_model [ Please input the path to model dir. ]

You will get a .json file which can be evaluated on COCO test server.